Determining Decision-Making Factors for Technology Adoption in the Construction Industry

Abstract

Construction organizations have been undergoing major efforts as the industry acknowledges the need to improve and change its traditional business-as-usual model. Inspired by the wave of technological advancement brought forward by the fourth industrial revolution (i.e., Industry 4.0 or its construction counterpart known as Construction 4.0), the need to investigate and successfully exploit technologies has never been more critical for construction researchers and practitioners. One research topic that remains limited pertains to the organizational aspect of successful technology adoption and the impact on the business environment in which the organization operates. To address the gap, the study utilizes the Technology-Organization-Environment (TOE) framework and synthesizes the existing research corpus to develop a comprehensive list of 23 decision-making factors for construction organizations to evaluate when adopting technologies. The study also offers an overview of existing research on the adoption of Construction 4.0 technologies, proposes 97 potential measures to evaluate the factors, and provides a discussion of the research trends. Accordingly, findings from this study can lay the foundation for decision-making processes and frameworks as technology adoption research grows and change efforts expand across the construction industry.

1. Introduction

The construction industry holds significant global importance, contributing substantially to the world’s economy. Projections indicate that by 2030, the industry is anticipated to contribute approximately 15% to the global GDP, with an 85% predicted increase in the volume of construction output reaching US$ 15.5 trillion worldwide [1, 2]. Despite its strong economic impact, the construction industry faces major problems that are affecting its performance. Notably, construction projects are highly likely to face cost and schedule overruns [3], design delays [4], safety problems [5], material and equipment issues [4, 6], and litigation [7]. Additionally, the industry faces resistance to change [8], repels younger workers [9, 10], lacks standardization [11], suffers from a high turnover rate [12], and has ineffective knowledge management systems [5]. Considering these challenges and the anticipated growth of the construction industry, the traditional business-as-usual model falls short of the industry’s expectations and is no longer sustainable [13, 14], especially as the industry’s annual productivity is capped at a staggeringly low growth of 1% annually versus a 2.8% total economic growth [15].

To address the challenges plaguing the construction industry, practitioners and researchers have prioritized the initiation of digitization and industrialization initiatives and explored the use of various technologies across the project lifecycle [16, 17]. Specifically over the past decade, the industry has made substantial investments in significant technological advancements and trends that have been introduced or pushed by the fourth industrial revolution, also known as Industry 4.0 [18]. Recent events have also pushed organizations to accelerate technology adoption, such as the surge in infrastructure investments, the demand for transparency within the supply chain, the rise in legislation to combat climate change, the arrival of Generation Z into the workforce, and the unexpected and unplanned outbreak of the COVID-19 pandemic that compelled organizations to conduct business in contactless environments and ensure the continuity of their operations [19, 20].

The rise in technology adoption led to “Construction 4.0”, a term coined to represent the mapping of Industry 4.0 into the construction industry [21]. The term was conceptualized in 2016 as part of a roadmap to digitize the construction industry of Europe and unleash the potential of automation, digital data, digital access, and connectivity across the construction supply chain players [22, 23]. Since then, research on Construction 4.0 has expanded to further understand the concept and its effect on the industry [24]. Table 1 summarizes the fundamental aspects of Construction 4.0 as pertained in the construction body of knowledge.

Aspect	Findings	References
Definition	Construction 4.0 is defined as the “digitization and industrialization of the construction industry” that (1) enables real-time vertical integration, horizontal integration, and end-to-end engineering, (2) advances construction processes through mechanization and automation, and (3) bridges the gap between the physical and cyber environments across the entire lifecycle of all types of assets.	[25, 26, 27]
Design principles	The design principles of Construction 4.0 include: Interconnection and interoperability through effective coordination, collaboration, and communication (human-human, human-machine, and machine-machine). Decentralization notably at the level of decision-making both internally (i.e., within the organizations’ cross-functional teams) and externally (among project players and supply chain players). Transparency through open, clear, and readily available information for relevant parties that require access to it. Technical assistance through embracing technologies that enhance processes, empower people, and boost project performance.	[28, 29]
Transformations	The transformations of Construction 4.0 include: Product transformation or transforming the physical asset that results from a construction project. Delivery transformation or transforming the delivery of the project from conceptualization through decommissioning and/or demolition. Digital transformation through enhancing the physical-cyber interaction. Mindset Transformation through focusing on people and encompassing “scientific thinking”.	[30]
Technologies	Construction 4.0 embraces, enables, and introduces a set of technological tools and technology concepts to the industry such as additive manufacturing, artificial intelligence, augmented and virtual reality, autonomous vehicles, blockchain, big data, cloud computing, cybersecurity, digital twins, drones, internet of things, geographic information systems, laser scanning, precast, prefabrication, modularization, robotics, and sensors.	[21, 27, 31, 32, 33, 34, 35, 36]

Table 1.

Construction 4.0 fundamental aspects.

1.1 Point of departure and motivation

The adoption of cutting-edge technology across industries can have a significant contribution to the industrial restructuring and economic transformation of the countries in which they operate, as well as increase the profits of organizations within these industries [37]. Organizational success can also largely depend on the organization’s ability to foster new technologies, especially that embracing the fourth industrial revolution’s technologies and achieving its components have shown great promise in enabling project success such as budget and schedule compliance, customer satisfaction, strategic accomplishments, and functionality or value achievements [38]. Thus, by embracing Construction 4.0 and effectively adopting its technologies, the construction industry can unlock a multitude of applications that bring benefits to all stakeholders involved in construction projects [18, 39]. This will also allow construction organizations to prepare for the fifth industrial revolution—i.e. Industry 5.0, which is beginning to unfold across various sectors [24].

However, the unique nature of the construction industry, particularly the high level of fragmentation, can make it challenging to adopt Construction 4.0 technologies and exploit their full potential [5]. It has been well documented that the construction industry suffers from vertical fragmentation with the various trade specialties, horizontal fragmentation with the dominance of small and medium-sized firms facing intense competition, and longitudinal fragmentation with the high turnover of clients and suppliers between one project and another [40, 41]. In such a fragmented context, even cutting-edge technologies developed for the construction industry can face potential failure, as technology providers and adopters must overcome numerous barriers including regulatory, commercial, cultural, organizational, and even psychological barriers [41, 42].

With the multi-faced and complex problems facing the industry, construction organizations must base technology adoption decisions on a sufficient volume of reliable and high-quality information [43]. Relying on such information can limit uncertainty, decrease the likelihood and severity of risks, and increase decision-makers confidence by better understanding consequences [43, 44]. This is especially important for technology adoption decisions, as the implementation of technologies comes with risks and challenges that may not only overshadow the success of the implementation but also extinguish the value of the technologies being adopted [45]. Thus, before adoption, decision-makers should evaluate the extent to which technologies of interest are a good fit with the organization’s business from various aspects.

Although previous studies have investigated technology adoption, the focus has mostly targeted the adoption from an individual level and how users can implement and exploit the potential of the technology [46, 47]. This creates a need to comprehensively understand and examine the decision-making factors that may influence the successful adoption of technologies from the perspective of the organization and the business environment in which it operates [13, 48, 49]. The holistic evaluation of such decision-making factors can allow organizations to justify financial investments [50], ensure that the technology can deliver the desired benefits over time [51], evaluate the impact of the technology on stakeholders and partners [52], assess the technology’s compliance with regulations and standards [53], gain more flexibility and efficiency when applying technologies to projects [54], and look “beyond the cognitive selling points of a technology” [55].

2. Objective and scope

2.1 Research objective

To address the gap, this chapter aims to develop a comprehensive list of decision-making factors for construction organizations to evaluate when adopting technologies. The chapter offers an overview of existing research on the adoption of Construction 4.0 technologies, extracts the decision-making factors, proposes potential measures for every factor, and provides a discussion of the research trends derived from the literature review. It is important to note that the scope of the chapter focuses on determining the decision-making factors rather than conducting a comparative analysis, assigning weights, quantifying, or evaluating the relative importance of every factor. The value of this comprehensive approach is two-fold, as it centralizes available information for industry practitioners and decision-makers while providing scholars with solid groundwork to expand on the decision-making research area. This comprehensive approach also serves to minimize redundancies and duplication of efforts within the dynamic field of technology adoption, especially as technologies continue to evolve and more studies are expected to emerge.

2.2 Selection of theoretical underpinning

To examine the concept of innovation adoption in organizations, research from different industries utilized several theories such as the Technology Adoption Model (TAM) [56], the theory of planning behavior (TPB) [57], the unified theory of acceptance and use of technology (UTAUT) [58], diffusion of innovation (DOI) [59], theory of reasoned action (TRA) [60], and technology-organization-environment (TOE) [61]. Theories of TAM, TBP, UTAUT, and TRA focus more on the individual level of analysis and the user perspective [62], making them unsuitable for the objective of this chapter. DOI and TOE investigate technology at the organizational level, but TOE is advantageous in studying adoption, use, and value creation as it accounts for the organization as well as the environment [63]. The TOE theory also has many strengths:

• TOE has a robust theoretical basis and solid empirical evidence as it was used in various technology adoption investigations across all industries [64];

• TOE has guided scholars in identifying the drivers of technology adoption from a corporate perspective, and the flexible nature of its factor makes the evaluation suitable for companies of all sizes and their intended use of the technology [65, 66];

• The extensive assessment of the external and internal capabilities of the technology as well as the internal and external factors that can affect the company makes TOE valuable in supporting sound decisions, predicting the success of adoption, and providing a comprehensive understanding of the adoption process [67, 68];

• The TOE aspects can be constantly extended to ensure the relevancy of the theory to timely business contexts [65];

• The TOE aspects present both opportunities and limitations for adoption, demonstrating broad applicability across several technological, industrial, and cultural contexts [69];

• TOE has proved its broad applicability across several technological industrial and cultural contexts [62, 69];

• TOE is the only framework that united both human and non-human factors by incorporating technology, organization, and environmental perspectives, and the literature offers strong empirical validation of the TOE framework [64, 70].

Thus, TOE was chosen as the underpinning theory for this study. In summary, the “Technological” aspect is concerned with technologies that are relevant to the organization including technologies available in the market as well as those being utilized by the organization [62]. The technology decision-making factors mostly stem from the DOI theory, and they allow the organization to assess new technologies and evaluate the added value and benefits that they can bring to the existing business [71]. The “Organizational” aspect refers to the inherent characteristics and resources of the organization [72]. The organizational decision-making factors allow the organization to “assess inwardly” and evaluate its strengths and weaknesses regarding the technologies of interest [71]. The “Environmental” aspect illustrates the arena or the setting in which the organization conducts its business [73]. The environmental decision-making factors provide the organization with a lens into the organization’s business ecosystem and understand the opportunities as well as challenges in the external environment surrounding the business [71].

3. Methodology

A systematic literature review (SLR) was conducted to identify the TOE decision-making factors that influence the adoption of Construction 4.0 technologies in the construction industry. SLR is a common and effective technique for investigating existing research and extracting knowledge on a targeted topic [74, 75]. The technique has extensively been used in construction management research, particularly for developing decision-making methods [76, 77, 78] and determining decision-making factors [54, 79, 80].

Since the outcome of SLR is dependent on the selected sample of studies, it is important to use “an objective and reproducible search system” [81, 82]. As such, this study utilized the Web of Science (WOS) database to extract the related studies as its one of the most comprehensive databases for systematic reviews, meta-analysis, and bibliometric analysis [83, 84], and contains access to unique journal titles, particularly in the fields of natural sciences and engineering [85]. A query-based search was performed to search the title, abstracts, and keywords of WOS publications using the keywords shown in Figure 1.

Figure 1.

The WOS query-based search (* indicates an asterisk used for search purposes).

The review process for extracting the decision-making factors and possible measures is illustrated in Figure 2. Studies that passed the title/abstract scan were reviewed for two aspects: (1) to determine if they were construction-related (i.e., the literature, surveys, case studies, and/or interviews were from the construction industry), and (2) if the technology was unique and relevant to Construction 4.0. This resulted in 22 publications from which the TOE decision-making factors were extracted. As for studies that had TOE decision-making factors and measures but were not construction-related or did not analyze a relevant technology, they were added to a database that was scanned to only extract suggested measures that can be used to evaluate the factors. The database resulted in 19 publications. All publications used are presented in Table 2.

Figure 2.

The review process for publications.

Database	Publications
Relevant publications	Aghimien et al. [86]; Attencia and Mattos [87]; Badi et al. [88]; Besklubova et al. [89]; Cai et al. [90]; Chaurasia and Verma [91]; Dashti and Viljevac-Vasquez [92]; Gan et al. [93]; Jadhav [94]; Katebi et al. [95]; Li et al. [96]; Lu and Deng [97]; Mabad et al. [64]; Muylle [98]; Na et al. [99]; Pan and Pan [100, 101]; Ram et al. [71]; Umar [102]; Viljakainen [66]; Wang et al. [103]; Wu et al. [104]; Yuan et al. [105]
Other publications (construction exclusive)	Chen et al. [106]; Jadhav [94]; Juan et al. [107]; Lee and Yu [108]; Li et al. [109]; Ma et al. [110]; Park et al. [111]; Tran et al. [112, 113]
Other publications (not construction exclusive)	Arnold et al. [114]; Bakici et al. [69]; Chandra and Kumar [115]; Gangwar et al. [63]; Ghaleb et al. [116]; Iranmanesh et al. [117]; Malak [118]; Tsai and Yeh [119]; Wong et al. [70]; Zhang et al. [120]

Table 2.

Publications in the above databases.

The following is a description of the review process illustrated in Figure 2. The study conducted by Pan and Pan [100] for instance used TOE to investigate the use of robotics in the Chinese construction industry. Since the paper covers a Construction 4.0 technology and relates to the construction industry, it was added to “relevant publications”. On the other hand, the study conducted by Ma et al. [110] utilized TOE to investigate social media use in the Chinese construction industry. Since this study was construction related but did not investigate a construction 4.0 technology, it was added to “other publications” (construction related). Similarly, Bakici et al. [69] and Iranmanesh et al. [117] investigated big data adoption in France and Malaysia respectively, but both studies surveyed experts from inside and outside the construction industry. Thus, both studies were added to “other publications” (not construction related). This database of “other publications” was used to support the development of measures for the various TOE factors. Since TOE construction-related research is scarce, a review of the measures utilized in other studies provided additional resources to formulate construction-related measures to evaluate the TOE factors under study.

To further understand and detect research trends among the decision-making factors, three heatmaps were created to visualize the distribution of the factors by type of investigation (i.e., whether the factor was mentioned directly or indirectly), by technology (i.e., the technology that the publication targeted), and by year (i.e., the year of publication). The frequency of occurrence of the factors across the three distributions serves as the database for the heatmaps, as utilizing frequency is another commonly used approach to offer knowledge and detect trends in SLR-based construction management studies [18, 121, 122].

4. Results and analysis

4.1 Review of relevant publications

Based on the SLR and as depicted in Figure 3, Blockchain was the most investigated technology with 4 studies, followed by robotics, 3D printing, precast, and sensors with 3 studies each, then Big Data and Augmented/Virtual Reality with 2 studies each, and finally artificial intelligence and cloud computing with 1 study each. The earliest publication on TOE with Construction 4.0 technologies was in 2019 where four studies utilized TOE to evaluate the adoption of Robotics, 3D printing, big data, and precast.

Figure 3.

Profile of the 22 relevant publications.

4.1.1 Artificial intelligence

The study by Na et al. [99] focused on the adoption and acceptance of AI-based technology in South Korean construction firms using a TOE-TAM model. Structural equation modeling (SEM) applied to survey data from 241 respondents showed that technological factors such as suitability, compatibility, and relative advantage had a positive influence on the adoption of AI-based technologies and the ease of use within the organization. However, environmental factors like social influence and competition did have any effect, which the study attributed to organizations’ “conservatism” as they slowly adopt and test the technology. As for organizational factors like culture and structure, they had a positive impact on end-users’ ease of use, but a negative one on the perceived usefulness of AI-based technology.

4.1.2 Augmented/virtual reality

Two studies investigated the adoption of augmented reality (AR) and virtual reality (VR). Dashti and Viljevac-Vasquez [92] interviewed 13 construction professionals in Sweden and concluded that the lack of knowledge about the benefits, a limited number of vendors, insufficient knowledge about the interactions of AR/VR with other technologies, a lack of management expertise, financial constraints, high-risk investments, a lack of encouragement from owners, and the terms of contracts are significant TOE factors affecting adoption. Viljakainen [66] also conducted interviews with professionals in Finland using a “Who, What, Where, How many, How much” approach. Results showed that technological factors such as limited features and capability and compatibility concerns can restrict AR usage, organizational factors such as strategic long-term planning, leadership support, commitment, and resources are critical for the success of AR, and environmental factors in terms of environment, competition, reputation, and striving for innovation can help promote AR. The study further highlighted that a “forced trigger” event like the COVID-19 pandemic can push reluctant companies to adopt AR in their operations [66].

4.1.3 Big data

Two studies were conducted to understand the factors affecting the adoption of Big Data in the construction industry. Ram et al. [71] synthesized existing literature to develop a conceptual TOE model that is positively associated with Big Data adoption in the industry. The model highlighted the importance of integrating Big Data with existing organization technologies like BIM and the relative advantage of utilizing Big Data in reaping gains including cost savings, speed, efficiencies, and informed decision-making. A second study by Chaurasia and Verma [91] gathered data from 365 Indian AEC firms and proposed a TOE-PSVAM model to understand the factors affecting Big Data adoption in construction firms as well as service firms. The results showed that firm size was significant for construction firms, readiness and top management support were significant for service firms, while data quality, complexity, and competitive pressure were significant for both construction and service firms [91].

4.1.4 Blockchain

Two studies utilized TOE to investigate the adoption of Blockchain among Chinese construction organizations. Using a TOE-TAM based study, the empirical results conducted by Wang et al. [103] on 256 survey responses showed that relative advantage, compatibility, competitive pressure, technological maturity, organizational readiness, and policy can impact the adoption of Blockchain in China. Competitive pressure had the greatest positive impact on adoption while organization readiness had a negative impact. The authors attributed the results to the fundamental changes and disruptions that Blockchain could bring to organizations and the traditional ways of conducting business [103]. Similar results were obtained by Li et al. [96] who developed a conceptual TOE model to analyze the determinants of Blockchain adoption in construction using partial least squares (PLM) SEM and fuzzy-set qualitative comparative analysis via data gathered from 244 respondents. Results added firm size and top management support to factors that can significantly influence blockchain adoption in China, and highlighted that further investigation is needed into complexity, trialability, and pressure from training partners [96]. In the UK, Badi et al. [88] identified and investigated TOE factors that influence the adoption of smart contracts through a survey on 104 professionals. Results of a regression model showed top management support as an organizational facilitator that significantly facilitates smart contracts, competitive pressure, and supply chain pressure as two environmental aspects that significantly facilitate smart contracts, and observability as a technology aspect that significantly limits smart contracts. The study also explained that the relative immaturity of smart contracts may have contributed to all technological aspects being non-significant, notably relative advantage for the lack of empirical benefits, and compatibility due to the nature of current construction project contracts [88]. The fourth Blockchain study conducted by Wu et al. [104] took a different approach as it utilized TOE to summarize challenges facing blockchain adoption. The study highlighted that success can depend on several factors such as scalability, security, compatibility, training, contractual collaborations, consensus on data sharing, and industry standards [104].

4.1.5 Cloud computing

Aghimien et al. [86] aimed to identify the determinants for using cloud computing and predict its usage in South African construction organizations. Data from 154 experts were analyzed using SEM and regression models, revealing six significant factors including three technology factors related to cost-effectiveness, availability, and compatibility, and three environmental factors related to client demand, competitor pressure, and trust in vendors. However, no organizational factors were found significant due to the high awareness of the technology and most organizations being either early or laggard adopters in at least one aspect of the technology [86].

4.1.6 Precast

The adoption of prefabricated building technologies is influenced by various factors. Gan et al. [93] used fuzzy cognitive maps to identify critical factors through in-depth interviews with 39 experts representing six key stakeholder groups. Three factors in specific—cost, market demand, and policies and regulations—were perceived as critical by all stakeholders. The study also provided practical suggestions to promote prefabricated building technologies including establishing an information exchange platform, issuing mandatory policies and regulations by the government, and simulating the market demand [93]. Yuan et al. [105] adopted a TOE-TAM model via a survey of 234 professionals in China and found that relative advantage, corporate social responsibility, and market demand are significantly positively related to increasing the perceived usefulness of the technology, while regulatory support and trading partner support can decrease the complexity of using it. Results also showed that compatibility, organizational readiness, top management support, and competitive pressure did not impact the ease of use or perceived usefulness of the technology [105]. Katebi et al. [95] also used a TOE-TAM model to quantitatively investigate the factors influencing the adoption of precast concrete components (PCC) in Iran. They found that two environmental factors were critical: competitive pressure where the trend to adopt PCC will accelerate once companies see their competitors succeeding and reaping benefits and government provides support through effective policies, regulations, and incentives [95].

4.1.7 Robotics

Two studies conducted by Pan and Pan [100, 101] identified key factors affecting adoption such as relative advantage in terms of performance metrics like quality and productivity, top management support to strategize and invest, organizational readiness through financial resources and human talent, competitive pressure, and market demand. Their 2020 study further highlighted the importance of organizational factors such as top management support and readiness in the early stages of adoption. Meanwhile, technological and environmental factors such as compatibility and collaboration with trade partners, become more influential at later stages [100]. Another study by Cai et al. [90] provided 11 influential TOE factors for the successful implementation of construction automation and robotics, including interdisciplinary cooperation and continuous feedback, communication among internal teams and external stakeholders, and strategy development to expand the use of robots across the organization. The study also noted that the increase in market demand for robots can attract technology providers to further develop use cases and in turn motivate the industry to increase adoption rates [90].

4.1.8 Wireless and sensing

Three studies used TOE to investigate the adoption of Radio Frequency Identification (RFID), intelligence surveillance systems, and intelligent asset management systems. The first study by Mabad et al. [64] surveyed 297 Information Technology (IT) staff in Australian construction firms to evaluate factors affecting RFID investment decisions using SME. The study found that relative advantage, compatibility, cost, firm size, top management support, government regulations, external support, and anticipated benefits are significant factors affecting RFID adoption. The study further derived practical implications and guidelines designed to help construction organizations adopt RFID like evaluating the compatibility of the organization’s IS infrastructure, promoting RFID and encouraging professionals to attend seminars, developing budgetary projections to address cost-effectiveness, procuring external consultancy for security concerns and facilitating trust, and identifying champions to lead and monitor RFID application and functionalities [64]. The second article by Lu and Deng [97] distributed questionnaires to 220 professionals in China to develop an acceptance model for intelligent surveillance systems in construction using TAM and TOE. The study found that the relevance of technology to job tasks, availability of training, availability of vendors and technical support, cost savings, government actions and incentives, technological support from vendors and IT, and privacy concerns are important factors affecting the acceptance of intelligent surveillance in the construction industry. Moreover, the study highlighted that such factors can help construction industry practitioners have a clearer understanding of the essential elements that affect the implementation of intelligent surveillance systems and guide organizations, governments, and technology developers to formulate strategies to promote the adoption of intelligent surveillance systems more effectively [97]. As for the third article, Attencia and Mattos [87] conducted interviews with 12 subject matter experts working on four construction projects within the energy infrastructure in Brazil to understand the factors affecting the adoption of intelligence asset management systems including tracking, monitoring, pointing, and positioning systems. Results revealed that technological factors with the strongest evidence include benefits for decision-making, risk analysis, and information management. Among the organizational factors, the strongest evidence pertained to attitudes towards innovation and creativity, organizational size, and the ability to implement and optimize technology, as well as integration into existing processes. Additionally, the most evident environmental factor was the support provided by technology providers during the asset management phase [87].

4.1.9 3D printing

The TOE factors influencing the adoption of 3D printing were evaluated using surveys, interviews, and literature reviews. The first study by Muylle [98] conducted in-depth interviews with 10 construction firms and labs in the Netherlands and Belgium, identifying 3D printing’s ability to integrate within current projects, durability, flexibility, and efficiency as major technological factors, management support, and resource availability as organizational factors, and market trends and government regulations and subsidies as environmental factors influencing adoption. Another investigation by Besklubova et al. [89] surveyed 82 global researchers and scholars to identify the factors that affect the adoption of 3D printing in construction. SEM showed that the top significant factors were compatibility with existing processes, ability to print materials that integrate with projects, complexity in operating and maintaining printers, and the industry’s need to simplify tasks and reduce transportation services and involved suppliers [89]. Moreover, Umar [102] conducted a systematic literature review of 137 articles published between 2000 and 2019 on 3D printing in construction, which highlighted technology-related factors such as compatibility and integration with CAD software and IT hardware, organizational factors such as readiness, management support, commitment, training, and IT infrastructure, and environmental factors such as competitive pressure, expectations from the market tendency, business partners, and government support. The study also highlighted the importance of financial resources to cover the costs related to machines, labor, and material [102].

4.2 Decision-making factors

A total of 23 decision-making factors were extracted from the 22 TOE-relevant studies. The factors are listed and defined in Table 3. Each factor is also presented with a list of potential measures compiled comprehensively from the publications shown in Table 2.

	Factor	Definition	Potential measures
Technology	Compatibility	The degree to which the technology is “consistent with the existing values, experiences, and needs of the company” [123].	Compatibility with culture Compatibility with mission and vision Compatibility with existing workflows Compatibility with existing capabilities
	Relative advantage	The degree that technology “is seen as better than the idea, program, or product it is replacing company” [123].	Better financial transactions and profit Better organizational networking advantage Better organizational performance Better organizational capabilities Better individual capabilities Better project performance Better data management and security
	Complexity	The degree to which it “is perceived as relatively difficult to understand or use” [123].	Complexity of technology implementation including hardware and software Complexity of technology integration into existing workflows and capabilities Complexity of technology operation
	Industry standards	Set of policies, regulations, or best practices presenting a structured adoption and implementation plan for a technology [124].	Availability of in-house standards Access to external standards Feasibility of developing standards if needed
	Trialability	The extent to which the technology “can be tried on a small scale before adoption, or experimented with on a limited basis company” [123].	Scale of trialability Investment for piloting and/or testing Level of risk and scalability Requirement for collaboration
	Observability	The extent to which the results of using the technology can provide tangible results that can be visible to other companies [123].	Technology market share Availability of a community of practice Frequency of updates or version releases Industry recommendations Tangible proven benefits
	Security and privacy	The measures and practices available to control access and protect information through authentication, authorization, accountability, data protection, disaster recovery, sharing, and the privacy of users [63].	Robust against threats Robust against risks Robust against vulnerability
Organization	Human resources	The demographic variables and capabilities of the people within the organization [119, 125].	Level of commitment Level of technical skills Level of digital literacy Percentage of turnover
	Financial resources	The monetary assets and capital available for investments and operations [126, 127].	Availability of funds to invest in hardware and software Availability of funds to invest and continuously support training Availability of funds for systems and infrastructure updates Availability of funds for maintenance and operation cost Availability of funds for required skills if needed
	Change culture	The shared values, beliefs, norms, and behaviors that shape the organization’s collective identity and guide the actions and interactions of its people [128].	Vision and policies for innovation and continuous improvement Focus on research, development, and technology watch Models for information sharing and funding Momentum for digitization and modernization
	Top management support	Active endorsement, involvement, and commitment of leadership in change efforts [63, 98, 125].	Awareness and encouragement of technology Level of involvement and visibility into the process Willingness to allocate resources Availability of change management platforms
	Information systems	The formal, sociotechnical, organizational system designed to collect, process, store, distribute, and interpret information in an organization [129].	Level of training readiness Level of hardware readiness Level of software readiness Level of integration readiness Level of workflow readiness Availability of data standards Availability of protocol for unexpected events (risks, failures, conflicts)
	Scale of operations	The organization’s characteristics like size, age (i.e. legacy or not), origin, location, and the number of customers and projects [115, 127, 130].	Scalability across the organization Applicability across organization Relevancy across organization
	Nature of projects	The inherent characteristics and attributes of the projects that the organization works on [119].	Scalability across project characteristics (locations, size, types) Applicability across project complexities
	Nature of workflows	The inherent characteristics and requirements of tasks that the organization will use the technology in [131].	Relevancy across project lifecycles or through project phases
	Availability of training	The accessibility and provision of training opportunities to embed or enhance the required skills and knowledge [63, 132].	Availability of in-house training programs Access to external training programs Need and availability of third-party involvement if needed Frequency of training Modularity and delivery of training Requirements for training
	Type of contracts	The type of contractual arrangements that the company utilizes with clients can enable or inhibit technology [133].	Applicability across project delivery systems Applicability across project contracts
Environment	Authorities and government influence	Role of governmental agencies and authorities in influencing the organization to adopt [91].	Availability of incentives Availability of subsidies Availability of tangible support Availability of intangible support Availability of mandate Comprehensiveness of regulations and legislation Extent of penalties
	Competitive pressure	The pressure resulting from the practices of competitors and the need to outperform competition and achieve superior performance in the marketplace [30].	Ability to improve performance Ability to grow in the market and expand in new ones Ability to customize services and offer new ones Ability to secure technology leads or early innovators Ability to grow customer base and clients Ability to recruit and retain talent Ability to enhance data-driven workflows
	Push from supply chain players, stakeholders, and trade partners	Pressure exerted by external players to push the organization to adopt [106, 127].	Extent of leverage gained from adoption Timing of investment Dependency of adoption on partners and supply chain Readiness of partners and supply chain
	Market demand	Capabilities to solve or assist the organization in addressing industry needs [13].	Ability to address legacy challenges Ability to react to current trends Ability to pull towards future visions
	Vendor access	Access to solution and technology providers and the services they provide [64, 134].	Requirement for customer support and vendor service Availability of customer support and vendor service Access to customer support and vendor service Type of customer support and vendor service
	Social responsibility	Commitment to act ethically, contribute positively to society, and promote sustainable practices [19].	Incentive or requirement to reduce carbon footprint Incentive or requirement to lower emissions Incentive or requirement to adopt green practices Implementation of construction waste management Need to empower society

Table 3.

Decision-making factors and potential measures.

Moreover, the heatmaps showing the distribution of the factors are presented in Figure 4. The first heatmap to the left shows the distribution by type of investigation—i.e., whether the factor was investigated directly or indirectly in the reviewed publication. The darker the color, the higher the frequency of the investigation type for the factor. The second heatmap in the middle shows the distribution by technology. The darker the color, the higher the frequency of occurrence of the factor in the papers that targeted that technology. The third heatmap to the right shows the distribution by year of publication. The darker the color, the higher the frequency of occurrence of the factor in studies published in that year. The objective of the heatmaps is to illustrate the research trends discussed in the next section of the chapter.

Figure 4.

Distribution of TOE decision-making factors (“n” represents the total number of papers for the investigated technology or year of publication).

5. Discussion

5.1 Technology decision-making factors

Starting with technology-related factors, relative advantage was the only technology factor that was investigated at least once for every technology, followed by complexity and compatibility which were investigated for every technology except cloud computing. The three factors were almost always investigated directly in research and maintained consistent or increased investigations between 2019 and 2022. In a traditional industry like construction, the domination of these three factors is expected because organizations are more likely to experiment with or adopt technologies that provide the maximum benefits and meet organizational needs with minimum complications and changes needed to the business model and organization infrastructures [135]. Similarly, technologies that can be highly reliable and functional with low maintenance requirements will be more attractive to organizations than complex ones that may become a burden on the business [136].

Two of the technology factors—Trialability and Observability—have a motivation component to them compared to the other factors which affect their investigation. Trialability was mostly investigated directly as it deals with a specific feature of the technology that allows users to pilot it or experiment with it on a limited scale, thus making it a dichotomous factor that is either available or not [137, 138]. Observability deals mostly with the technology’s reputation across the industry which explains it’s the nature of its indirect investigation, and it is only evident in research that relates to technologies that have been around long enough for the industry to develop a perception of them [137]. Industry standards is another factor that received attention but was mostly investigated indirectly. This reflects on one of the industry’s major issues—i.e. lack of standardization and calls for a more direct investigation into the factor [11, 139].

While security and privacy was only investigated for four technologies, it can be noticed that the factor had a relative increase over time and was investigated in all Blockchain studies. This factor is expected to gain even more momentum as more industry research and legal discussions are being tailored towards confidentiality, integrity, fraud, theft, risks when accessing common platforms, data ownership and governance, and security breaches [140, 141, 142]. This also becomes especially important with technologies that create, store, and share sensitive and private information like Blockchain which encompasses digital ledgers and financial transactions [143].

5.2 Organization decision-making factors

As for organization-related factors, human resources and top management support were investigated at least once for every technology and maintained consistent or increased investigations between 2019 and 2022. Financial resources was also a heavily investigated factor across all technologies except Big Data and was investigated in every study published in 2021 and 2022. The domination of these three factors is no surprise as organizational determinants like budgets, high cost of implementation, preference for short-term profits over long-term ROIs, labor shortage, scarcity of skills and required talents, hesitancy to adopt, and need for management support and commitment remain major barriers facing technology adoption and slowing down the digital transformation of the fourth industrial revolution in construction [5, 144, 145, 146]. Moreover, organizations that strengthen human resources and financial investments can improve their technology’s output capacity and technological achievements [37]. In addition to the three discussed factors, the trend for information resources has also been increasing. This trend is expected to continue and gain more momentum due to the vital role information systems play in the organization’s response to internal and external changes [125]. Moreover, information systems enable the organization to effectively manage and utilize the information generated by the new technology and integrate it with the existing information from people, machinery, equipment, tools, materials, and projects [125, 147].

Another notable notice is change culture being a common factor that was extensively investigated. Several studies regarding innovation have highlighted the importance of organizational culture and the role it plays in successfully selecting and implementing technologies; such a culture should promote openness, adaptability, and receptiveness to change, foster a supportive and innovative environment, and create a culture that embraces change within the organization by incentivizing and supporting new technology efforts, building multidisciplinary teams, celebrating progress, and addressing resistance and concerns [148, 149, 150]. Innovation should also be fostered in the organization’s core beliefs, behaviors, and practices such as active innovation champions, innovation training, technology watch, and knowledge management [150, 151].

While the focus on the availability of training has been low, it remains an important factor for the construction industry because of its low level of digitization and high tendency to resist change [13]. More investigations should target this since training can instruct employees, reduce anxiety and stress, provide motivation and a better understanding of benefits, reduce ambiguity, improve the perceived ease of use and usefulness, and open the door for improvements [63, 132]. As for scale of operations, nature of projects, nature of workflows, and type of contracts, these four factors are more customized to the construction industry’s unique nature and business-as-usual ways. While the factors appear occasionally in research, the low investigation of type of contracts reflects on the continued domination of traditional project delivery methods in the construction industry and the rising need to adopt technology and digitize processes across all types of contracts used in the organizations’ projects [152, 153].

5.3 Environment decision-making factors

Regarding environment-related factors, authorities and government influence, competitive pressure, and push from external partners were investigated at least once for every technology, almost always investigated directly, and maintained consistent or increased investigations between 2019 and 2022. The construction industry is generally considered to be competitive as firms compete over contracts and projects. As such, the pressure resulting from the practices of competitors and the need to gain competitive advantage can drive firms to change their business-as-usual mindset and innovate and adopt technologies that can “alter the rules of the competition and change the competitive playing field” [100, 106, 154]. At the same time, despite the competitive nature, most projects require collaborations between multiple firms with different areas of expertise, which in turn creates an incentive to adopt technology—pressure from owners, push from clients, incentives by consultants, pressure from trading partners across the supply chain, and project stakeholders adopting a certain technology can all push organizations to adopt [106, 127]. As for the government’s roles, governmental agencies and authorities can affect the diffusion of technologies by passing rules, policies, and regulations, as well as providing incentives and opportunities to create a perception of the values associated with the technology [91, 106].

A drop in the investigation of market demand can also be witnessed—i.e., the factor that reflects the technologies’ capabilities to solve or assist the industry in tackling major hurdles such as labor shortages, project overruns, and low productivity. In contrast, an increase is witnessed in the investigation of vendor access—i.e., the factor that reflects access to technology providers and the services that they offer. The trends of the two factors reflect the shift in the industry’s perception regarding technology, where it is no longer “nice to have” but rather a “must” and a “necessity” for success [155]. Thus, while market demand can still accelerate technology adoption, it becomes less of a primary driver because the industry acknowledges the need for digitization [5, 156]. This also shifts the focus towards technology and solution providers who facilitate and expedite the adoption of technologies in organizations and provide them with licenses, partnering arrangements, software updates, and track records to prove the value of their technology [64, 134].

On another note, social responsibility was the only environment-related factor that was under-researched and mostly investigated indirectly. However, construction organizations and construction projects serve diverse communities and thus, may “either feel a voluntary obligation to a society based on social expectations, norms, and codes of conduct”, or be “placed in situations where they cannot ignore the social community due to rising public pressures” and growing need for sustainable and green construction [127, 157]. Moreover, more research is providing evidence of digitalization’s ability to enable better eco-friendly performance [158], by investigating how investment behavior is affected by an organization’s social performance and responsibilities [159], and bringing organizations closer to their communities [19, 160]. As such, the social responsibility factor remains relevant and requires increased attention in research.

6. TOE factors and the path towards Construction 5.0

As industries switch from the technology-driven Industry 4.0 transformation to a value-driven Industry 5.0 transformation, organizations need to repurpose or readjust their journey to align with the core values of Industry 5.0 [161]. The three core values of Industry 5.0 include a human-centric approach that prioritizes humans and the use technology to support people, sustainability that respects planetary boundaries to meet today’s needs without compromising those of future generations, and resilience achieved through the development of a higher degree of robustness [162]. These core values are interrelated with the concept of Society 5.0, which seeks to create a human-centric, knowledge-intensive, and data-driven society that seamlessly integrate the cyberspace with physical space while balancing economic development with resolving societal and environmental issues [162, 163]. Consequently, the shared challenges and opportunities of Industry 5.0 and Society 5.0 will manifest in different aspects, including human-cyber physical systems, human digital twin, greentelligent manufacturing, human-robot collaboration, as well as future jobs and future workers [164].

Thus, the technological, organizational, and environmental decision-making factors discussed in this chapter will remain pivotal for the construction industry as it transitions to Industry 5.0, or Construction 5.0. Decision-making in Construction 5.0 will aim to integrate human-centric, resilient, and sustainable approaches into existing industry processes including design, construction, delivery, and supply chain [165]. Accordingly, the 5.0 transformation will continue to be influenced by culture, leadership, collaboration, cooperation, workers expertise, technology infrastructure, as well the organizational context in terms of size, core business, and project types [16].

Moreover, the Industry 5.0 core values focus on the use of advanced technology to empower—rather than replace—people across industries [162]. As such, Construction 5.0 will emphasize the importance of human involvement and the value of human input by “prioritizing human needs and interests as the foundation of the construction process”, and “ensuring that technological advancements align with human wellbeing and sustainability goals” [166]. A notable example is the proper use of the TOE factors discussed in this chapter to strengthen the adoption of robotics in the Construction 5.0 era and increase safety, efficiency, and quality resulting from the “intuitive interactions between robots and humans within the complex construction environments” [24].

7. Conclusion

This research aimed to develop and provide a comprehensive list of decision-making factors for technology adoption in the construction industry. The Technology-Organization-Environment (TOE) framework was utilized to perform a systematic literature review and examine the existing work on the adoption of Construction 4.0 technologies. A total of 22 TOE-relevant studies were retrieved from the Web of Science database to extract the decision-making factors, all published between 2019 and 2022. The reviewed papers used different quantitative and qualitative approaches to investigate the TOE factors affecting the adoption of nine Construction 4.0 technologies including artificial intelligence, augmented reality and virtual reality, big data, blockchain, cloud computing, precast, robotics, wireless and sensing technologies, and 3D printing.

The review of the studies resulted in a comprehensive list of 23 decision-making factors, alongside 97 potential measures for the factors extracted from the relevant publications and 19 other related studies. Of the 23 decision-making factors, seven were technology factors, 10 were organization factors, and six were environment factors. Compatibility, relative advantage, and complexity were the most evident technology factors which construction organizations considered when selecting a technology, as found in the literature synthesis. Additionally, trends depicted in the developed heatmaps showed that security and privacy was gaining more momentum. As for organization factors, human and financial resources were heavily investigated, and significant attention was given for change culture and top management support. It was also noticed that some factors were construction industry exclusive such as nature of projects and nature of workflows. Regarding environment factors, government incentives and competitive pressure were the most investigated factors, while social responsibility was the least investigated.

The study is twofold as of value for both researchers and practitioners. First, the comprehensive list of decision-making factors introduced in this chapter can be used to develop decision-making tools, methods, and/or frameworks regarding technology adoption in construction. Second, the revision provided in this chapter provides a centralized resource for decision-makers to pinpoint the existing work in understanding the existing work in the decision-making technology field. Third, the research trends discussed in this chapter can lay the foundation for future work and eliminate reduces as they highlight the factors that have been heavily investigated, the factors that are gaining momentum, and the factors that need more attention. The chapter also comes with certain limitations. The identified papers from the systematic literature review are English-only publications from one research database (i.e., Web of Science). Moreover, the scope of the chapter focused only on determining the decision-making factors without any attempts to evaluate, provide weights, compare, and quantify the factors, or quantitatively compare the impact of those factors on the decision-making process. This opens the door for further studies that can build on this work by conducting quantitative analysis that can evaluate the impact of the factors on the decision-making process in the construction industry.